Monthly Archives: February 2019

Global Temperature Reanalysis Baseline Comparisons

I have been downloading the daily average global surface air temperature data initial condition output for the Sigma 0.995 level from the National Centers for Environmental Prediction and National Center for Atmospheric Research Reanalysis 1 (NCAR R1) cooperative effort.  This output is still being updated daily about 2 days behind the current day and extends back to 1948.  So far I have downloaded and processed the daily temperature output back to 1979.  The Sigma 0.995 level corresponds to the pressure altitude at 99.5 % of the surface air pressure, which is roughly about 50 meters above ground level.  The actual height above ground level varies somewhat depending on atmospheric conditions.  The NCAR R1 model uses a 2.5 degree latitude by 2.5 degree longitude grid.  In this post I am comparing the resulting global mean surface air temperature anomalies (GMSATA) for several time periods and different reference baseline periods.  See the Methods section at the end for calculation details and links to sources.

The first two graphs, Figures 1 and 2, display NCAR R1 daily average GMSATA time series output for two different reference baselines.  I was expecting the older 1979-2000 baseline to show higher temperature anomalies than the much more recent 2011-2015 baseline as can be seen in the graphs, but I was surprised that the baseline shift also changed the seasonal pattern across each year.  Apparently the seasonal patterns have shifted from one baseline period to another.  Figure 3 is provided to compare the daily average 2-meter above ground level surface air temperature anomalies from the more modern Climate Forecast System Reanalysis (CFSR)  to the NCAR R1 output.  The CFSR has a higher resolution of 0.5 degree latitude by 0.5 degree longitude.  See the Daily Updates page, which can be accessed from the menu bar at the top of this page, for more information about the daily CFSR output.

Figure 1. NCAR R1 GMSATA daily 2018 January through 2019 February 13, referenced to 2011-2015 baseline

Figure 2. NCAR R1 GMSATA daily 2018 January through 2019 February 13, referenced to 1979-2000 baseline

Figure 3. CFSR GMSATA daily 2018 January through 2019 February 14, referenced to 1979-2000 baseline

I also calculated GMSATA for two other reference baseline periods, 1994-2013 and 1981-2010.  The time series results for all four reference baseline periods for 2018-2019 are plotted together in Figure 4.  They all converge around January-February and diverge the most around September-October.  I’m not sure why.

I included the 1994-2013 reference period used by Nick Stokes for reporting daily averages here and the 1979-2000 period used by the University of Maine Climate Reanalyzer here.  The 1981-2010 period is the most recent three decade (30-year) period commonly used for climatological data reporting.  I previously used the 2011-2015 period for comparing monthly NCAR R1 versus CFSR temperature anomalies here.

Figure 4. NCAR R1 GMSATA 2018 through 2019 February 13 reference baseline comparisons

The next three graphs, Figures 5 through 7, are like the first three graphs, but covering a longer time period, from 2014 to 2019 so far.  Again the general patterns are similar, but the details differ.

Figure 5. NCAR R1 GMSATA daily 2014 through 2019 February 13, referenced to 2011-2015 baseline

Figure 6. NCAR R1 GMSATA daily 2014 through 2019 February 13, referenced to 1979-2000 baseline

Figure 7. CFSR GMSATA daily 2014 through 2019 February 14, referenced to 1979-2000 baseline

The last two graphs in Figures 8 and 9 cover a longer time period, for the current century so far, beginning in 2001.  I do not yet have CFSR daily averages for all of this period, so only the NCAR R1 results for two different reference periods are presented.

Figure 8. NCAR R1 GMSATA daily 2001 through 2019 February 13, referenced to 2011-2015 baseline

Figure 9. NCAR R1 GMSATA daily 2001 through 2019 February 13, referenced to 1979-2000 baseline

Overall, this exercise goes to show that changing reference baseline periods for daily GMSATA does cause quite a bit of variation in the results – more than I expected.  However, the general trends as indicated by the running 365-day averages did not appear to be affected by changing baselines.


For the NCAR R1 daily averages I downloaded the gridded Sigma 0.995 level temperature output which is provided in compacted annual files (thanks to Nick Stokes for providing the link below on his blog).  I used the National Aeronautic and Space Administration (NASA) Panopoly program to extract the temperature grids from the compacted data files and then loaded the daily temperature grid data into spreadsheets by year.  For each day I calculated daily averages by latitude zone, weighted by area, to compile zonal and global averages.

I calculated the reference period baseline averages for each day and used centered running 5-day averages to smooth the baseline results.  Once the reference baseline temperatures were calculated, the temperature anomalies were calculated for each day by subtracting the reference baseline value for the day from the actual daily temperature average for that day.  For the temperature anomalies I also calculated running 365-day and 91-day averages to show annual and seasonal scale tendencies.

NCAR R1 annual compacted files with daily global temperature grids:

NASA Panoply program:




GOES-17 Is Now Operational

“In its new role, GOES-17 will serve as NOAA’s primary geostationary satellite for detecting and monitoring Pacific storm systems, fog, wildfires, and other weather phenomena that affect the western United States, Alaska, and Hawaii.”

“The latest milestone for GOES-17 comes exactly eleven months after the satellite first reached its geostationary orbit 22,000 above Earth. Launched March 1, 2018, GOES-17 is NOAA’s second advanced geostationary weather satellite and the sister satellite to GOES-16 (also known as GOES East). Together the two satellites provide high-resolution visible and infrared imagery as well as lightning observations of more than half the globe – from the west coast of Africa to New Zealand, and from near the Arctic Circle to the Antarctic Circle.”

Large winter storm system moving into California 2019 February 2:

High resolution animation of clouds around Hawaii 2019 January 15:

High resolution animation of brown smoke plume blowing toward the west from the large from the Woolsey Fire west of Los Angeles  in California, as high level cirrus clouds above blow to the east on 2018 November 13:

Read more here.

Real-time images here.

The climate is changing!

The climate is changing!
The climate is changing!
The climate is changing!

And its all YOUR FAULT!!!

Say the Chicken Littles of today.

Mostly for pride, prestige, fame, and funding.
And to promote potentially catastrophic policies.
All in the false pretense of “green” and “renewable” energy.
Endeavors that will enrich the Chicken Littles promoting them.
Ironically these policies are not likely to stop climate change.
But will instead impoverish and endanger most of us.
Wasting huge amounts of hard earned tax payer dollars.

And to top it all off …
The hidden Chicken Little motto:
Do as I say, not as I do.
Hypocrisy at its best.

Chicken Littles of the world are in a class of their own.
Not subject to the rules they espouse for the rest of us.
They say we must quit using fossil fuels.
But that is not what they do:
Flying in private jets. Living in huge walled mansions.
Driving expensive cars. Living lavish lifestyles.
All provided by fossil fuels.

Climate change is now blamed for almost everything bad.
Never mind digging for real causes that are ignored.

Consequently, “climate change” has become a religion.
We must have faith in the sacred climate models.
Never mind that they have not been validated.
The unfaithful are persecuted as “deniers”.

When will the gullible public learn?
When lies are told often enough.
They are perceived as truth.
Buyer beware.

How the climate models fare

The graph below provided by Clive Best compares global surface air temperature anomaly projections from a large number of climate model runs for different Representative Concentration Pathways (RCPs) for carbon dioxide versus two estimates of global surface air temperature anomalies based on actual temperature measurements from Cowtan and Way and the UK Hadley Climate Research Unit Temperatures (HadCRUT4.6).  Notice that the measurement based estimates are indicating global temperatures at the low end of the model projections.

Not at all alarming.

Actual climate model temperature projections

Another graph from Clive Best below shows direct climate model output of projected global average surface air temperatures, indicating a very wide range in temperatures that is hidden by using temperature anomalies in the graph above.  Another sign that the climate models are not representing the real world very well.

Graphs sourced here, thanks to Clive Best.

And if you think climate is changing now, take a look at how much climate has changed in the last 3 million years without any help from humans:
Paleo Climate